
import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowerCamelCase_    :			List[Any]    =	16
lowerCamelCase_    :			Union[str, Any]    =	32







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase = 16    )	->						int:
				UpperCamelCase_:				Dict						=    AutoTokenizer.from_pretrained("""bert-base-cased"""    )
				UpperCamelCase_:				List[Any]						=    DatasetDict(
				    {
				        """train""": dataset["""train"""].select(lowerCamelCase    ),
				        """validation""": dataset["""train"""].select(lowerCamelCase    ),
				        """test""": dataset["""validation"""],
				    }    )

				def tokenize_function(lowerCamelCase    ):
								# max_length=None => use the model max length (it's actually the default)
								UpperCamelCase_:				str						=    tokenizer(examples["""sentence1"""]  ,     examples["""sentence2"""]  ,     truncation=lowerCamelCase  ,     max_length=lowerCamelCase    )
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								UpperCamelCase_:				Union[str, Any]						=    datasets.map(
								    lowerCamelCase  ,     batched=lowerCamelCase  ,     remove_columns=["""idx""", """sentence1""", """sentence2"""]  ,     )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				UpperCamelCase_:				Optional[Any]						=    tokenized_datasets.rename_column("""label"""  ,     """labels"""    )

				def collate_fn(lowerCamelCase    ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								UpperCamelCase_:				Union[str, Any]						=    1_28 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												UpperCamelCase_:				Optional[int]						=    16
								elif accelerator.mixed_precision != "no":
												UpperCamelCase_:				Optional[int]						=    8
								else:
												UpperCamelCase_:				Any						=    None

								return tokenizer.pad(
								    lowerCamelCase  ,     padding="""longest"""  ,     max_length=lowerCamelCase  ,     pad_to_multiple_of=lowerCamelCase  ,     return_tensors="""pt"""  ,     )

				# Instantiate dataloaders.
				UpperCamelCase_:				str						=    DataLoader(
				    tokenized_datasets["""train"""]  ,     shuffle=lowerCamelCase  ,     collate_fn=lowerCamelCase  ,     batch_size=lowerCamelCase    )
				UpperCamelCase_:				Optional[int]						=    DataLoader(
				    tokenized_datasets["""validation"""]  ,     shuffle=lowerCamelCase  ,     collate_fn=lowerCamelCase  ,     batch_size=lowerCamelCase    )

				UpperCamelCase_:				Optional[Any]						=    DataLoader(
				    tokenized_datasets["""test"""]  ,     shuffle=lowerCamelCase  ,     collate_fn=lowerCamelCase  ,     batch_size=lowerCamelCase    )

				return train_dataloader, eval_dataloader, test_dataloader







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						Optional[Any]:
				# New Code #
				UpperCamelCase_:				List[Any]						=    []
				# Download the dataset
				UpperCamelCase_:				Dict						=    load_dataset("""glue"""  ,     """mrpc"""    )
				# Create our splits
				UpperCamelCase_:				Dict						=    StratifiedKFold(n_splits=int(args.num_folds    )    )
				# Initialize accelerator
				UpperCamelCase_:				int						=    Accelerator(cpu=args.cpu  ,     mixed_precision=args.mixed_precision    )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				UpperCamelCase_:				int						=    config["""lr"""]
				UpperCamelCase_:				str						=    int(config["""num_epochs"""]    )
				UpperCamelCase_:				Any						=    int(config["""seed"""]    )
				UpperCamelCase_:				int						=    int(config["""batch_size"""]    )

				UpperCamelCase_:				int						=    evaluate.load("""glue"""  ,     """mrpc"""    )

				# If the batch size is too big we use gradient accumulation
				UpperCamelCase_:				Optional[Any]						=    1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								UpperCamelCase_:				Tuple						=    batch_size // MAX_GPU_BATCH_SIZE
								UpperCamelCase_:				Tuple						=    MAX_GPU_BATCH_SIZE

				set_seed(lowerCamelCase    )

				# New Code #
				# Create our folds:
				UpperCamelCase_:				Tuple						=    kfold.split(np.zeros(datasets["""train"""].num_rows    )  ,     datasets["""train"""]["""label"""]    )
				UpperCamelCase_:				Optional[int]						=    []
				# Iterate over them
				for i, (train_idxs, valid_idxs) in enumerate(lowerCamelCase    ):
								UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Union[str, Any]						=    get_fold_dataloaders(
								    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     )
								# Instantiate the model (we build the model here so that the seed also control new weights initialization)
								UpperCamelCase_:				Any						=    AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""  ,     return_dict=lowerCamelCase    )

								# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
								# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
								# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
								UpperCamelCase_:				Any						=    model.to(accelerator.device    )

								# Instantiate optimizer
								UpperCamelCase_:				int						=    AdamW(params=model.parameters()  ,     lr=lowerCamelCase    )

								# Instantiate scheduler
								UpperCamelCase_:				int						=    get_linear_schedule_with_warmup(
								    optimizer=lowerCamelCase  ,     num_warmup_steps=1_00  ,     num_training_steps=(len(lowerCamelCase    ) * num_epochs) // gradient_accumulation_steps  ,     )

								# Prepare everything
								# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
								# prepare method.
								UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				List[str]						=    accelerator.prepare(
								    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )

								# Now we train the model
								for epoch in range(lowerCamelCase    ):
												model.train()
												for step, batch in enumerate(lowerCamelCase    ):
																# We could avoid this line since we set the accelerator with `device_placement=True`.
																batch.to(accelerator.device    )
																UpperCamelCase_:				Dict						=    model(**lowerCamelCase    )
																UpperCamelCase_:				Union[str, Any]						=    outputs.loss
																UpperCamelCase_:				int						=    loss / gradient_accumulation_steps
																accelerator.backward(lowerCamelCase    )
																if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

												model.eval()
												for step, batch in enumerate(lowerCamelCase    ):
																# We could avoid this line since we set the accelerator with `device_placement=True`.
																batch.to(accelerator.device    )
																with torch.no_grad():
																				UpperCamelCase_:				List[Any]						=    model(**lowerCamelCase    )
																UpperCamelCase_:				List[str]						=    outputs.logits.argmax(dim=-1    )
																UpperCamelCase_, UpperCamelCase_:				Optional[int]						=    accelerator.gather_for_metrics((predictions, batch["""labels"""])    )
																metric.add_batch(
																    predictions=lowerCamelCase  ,     references=lowerCamelCase  ,     )

												UpperCamelCase_:				List[str]						=    metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F'''epoch {epoch}:'''  ,     lowerCamelCase    )

								# New Code #
								# We also run predictions on the test set at the very end
								UpperCamelCase_:				List[Any]						=    []
								for step, batch in enumerate(lowerCamelCase    ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device    )
												with torch.no_grad():
																UpperCamelCase_:				Tuple						=    model(**lowerCamelCase    )
												UpperCamelCase_:				int						=    outputs.logits
												UpperCamelCase_, UpperCamelCase_:				Union[str, Any]						=    accelerator.gather_for_metrics((predictions, batch["""labels"""])    )
												fold_predictions.append(predictions.cpu()    )
												if i == 0:
																# We need all of the test predictions
																test_references.append(references.cpu()    )
        # Use accelerator.print to print only on the main process.
								test_predictions.append(torch.cat(lowerCamelCase  ,     dim=0    )    )
								# We now need to release all our memory and get rid of the current model, optimizer, etc
								accelerator.free_memory()
				# New Code #
				# Finally we check the accuracy of our folded results:
				UpperCamelCase_:				List[Any]						=    torch.cat(lowerCamelCase  ,     dim=0    )
				UpperCamelCase_:				Tuple						=    torch.stack(lowerCamelCase  ,     dim=0    ).sum(dim=0    ).div(int(args.num_folds    )    ).argmax(dim=-1    )
				UpperCamelCase_:				Optional[int]						=    metric.compute(predictions=lowerCamelCase  ,     references=lowerCamelCase    )
				accelerator.print("""Average test metrics from all folds:"""  ,     lowerCamelCase    )







def 							A__       (    )	->						Optional[int]:
				UpperCamelCase_:				Dict						=    argparse.ArgumentParser(description="""Simple example of training script."""    )
				parser.add_argument(
				    """--mixed_precision"""  ,     type=lowerCamelCase  ,     default=lowerCamelCase  ,     choices=["""no""", """fp16""", """bf16""", """fp8"""]  ,     help="""Whether to use mixed precision. Choose"""
				    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
				    """and an Nvidia Ampere GPU."""  ,     )
				parser.add_argument("""--cpu"""  ,     action="""store_true"""  ,     help="""If passed, will train on the CPU."""    )
				# New Code #
				parser.add_argument("""--num_folds"""  ,     type=lowerCamelCase  ,     default=3  ,     help="""The number of splits to perform across the dataset"""    )
				UpperCamelCase_:				Dict						=    parser.parse_args()
				UpperCamelCase_:				str						=    {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
				training_function(lowerCamelCase  ,     lowerCamelCase    )


if __name__ == "__main__":
							main()








import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowerCamelCase_    :			int    =	16
lowerCamelCase_    :			str    =	32







def 							A__       (    lowerCamelCase    )	->						int:
				return int(x / 2**20    )

class 							_UpperCamelCase					:






			'''simple docstring'''

			def __enter__(							self			:							int   ):
							gc.collect()
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
							UpperCamelCase_:				str						=    torch.cuda.memory_allocated()
							return self




			def __exit__(							self			:							List[Any]    ,						*snake_case_			:							Union[str, Any]   ):
							gc.collect()
							torch.cuda.empty_cache()
							UpperCamelCase_:				List[str]						=    torch.cuda.memory_allocated()
							UpperCamelCase_:				int						=    torch.cuda.max_memory_allocated()
							UpperCamelCase_:				Optional[int]						=    bamb(self.end - self.begin   )
							UpperCamelCase_:				Tuple						=    bamb(self.peak - self.begin   )
							# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")







def 							A__       (    lowerCamelCase  ,     lowerCamelCase = 16  ,     lowerCamelCase = "bert-base-cased"  ,     lowerCamelCase = 3_20  ,     lowerCamelCase = 1_60  ,     )	->						Dict:
				UpperCamelCase_:				str						=    AutoTokenizer.from_pretrained(lowerCamelCase    )
				UpperCamelCase_:				List[str]						=    load_dataset(
				    """glue"""  ,     """mrpc"""  ,     split={"""train""": F'''train[:{n_train}]''', """validation""": F'''validation[:{n_val}]'''}    )

				def tokenize_function(lowerCamelCase    ):
								# max_length=None => use the model max length (it's actually the default)
								UpperCamelCase_:				Optional[Any]						=    tokenizer(examples["""sentence1"""]  ,     examples["""sentence2"""]  ,     truncation=lowerCamelCase  ,     max_length=lowerCamelCase    )
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				UpperCamelCase_:				Dict						=    datasets.map(
				    lowerCamelCase  ,     batched=lowerCamelCase  ,     remove_columns=["""idx""", """sentence1""", """sentence2"""]  ,     load_from_cache_file=lowerCamelCase    )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				UpperCamelCase_:				int						=    tokenized_datasets.rename_column("""label"""  ,     """labels"""    )

				def collate_fn(lowerCamelCase    ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								if accelerator.distributed_type == DistributedType.TPU:
												return tokenizer.pad(lowerCamelCase  ,     padding="""max_length"""  ,     max_length=1_28  ,     return_tensors="""pt"""    )
								return tokenizer.pad(lowerCamelCase  ,     padding="""longest"""  ,     return_tensors="""pt"""    )

				# Instantiate dataloaders.
				UpperCamelCase_:				List[str]						=    DataLoader(
				    tokenized_datasets["""train"""]  ,     shuffle=lowerCamelCase  ,     collate_fn=lowerCamelCase  ,     batch_size=lowerCamelCase    )
				UpperCamelCase_:				Optional[Any]						=    DataLoader(
				    tokenized_datasets["""validation"""]  ,     shuffle=lowerCamelCase  ,     collate_fn=lowerCamelCase  ,     batch_size=lowerCamelCase    )

				return train_dataloader, eval_dataloader







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						Union[str, Any]:
				# Initialize accelerator
				UpperCamelCase_:				Any						=    Accelerator()

				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				UpperCamelCase_:				str						=    config["""lr"""]
				UpperCamelCase_:				Any						=    int(config["""num_epochs"""]    )
				UpperCamelCase_:				Tuple						=    int(config["""seed"""]    )
				UpperCamelCase_:				Dict						=    int(config["""batch_size"""]    )
				UpperCamelCase_:				Dict						=    args.model_name_or_path

				set_seed(lowerCamelCase    )
				UpperCamelCase_, UpperCamelCase_:				int						=    get_dataloaders(lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     args.n_train  ,     args.n_val    )

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				UpperCamelCase_:				Any						=    AutoModelForSequenceClassification.from_pretrained(lowerCamelCase  ,     return_dict=lowerCamelCase    )

				# Instantiate optimizer
				UpperCamelCase_:				Tuple						=    (
				    AdamW
				    if accelerator.state.deepspeed_plugin is None
				    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
				    else DummyOptim
				)
				UpperCamelCase_:				List[Any]						=    optimizer_cls(params=model.parameters()  ,     lr=lowerCamelCase    )

				if accelerator.state.deepspeed_plugin is not None:
								UpperCamelCase_:				Any						=    accelerator.state.deepspeed_plugin.deepspeed_config[
								    """gradient_accumulation_steps"""
								]
				else:
								UpperCamelCase_:				List[Any]						=    1
				UpperCamelCase_:				Optional[int]						=    (len(lowerCamelCase    ) * num_epochs) // gradient_accumulation_steps

				# Instantiate scheduler
				if (
				    accelerator.state.deepspeed_plugin is None
				    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
				):
								UpperCamelCase_:				List[Any]						=    get_linear_schedule_with_warmup(
								    optimizer=lowerCamelCase  ,     num_warmup_steps=0  ,     num_training_steps=lowerCamelCase  ,     )
				else:
								UpperCamelCase_:				Dict						=    DummyScheduler(lowerCamelCase  ,     total_num_steps=lowerCamelCase  ,     warmup_num_steps=0    )

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				str						=    accelerator.prepare(
				    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )

				# We need to keep track of how many total steps we have iterated over
				UpperCamelCase_:				str						=    0
				# We also need to keep track of the stating epoch so files are named properly
				UpperCamelCase_:				Dict						=    0

				# Now we train the model
				UpperCamelCase_:				Dict						=    {}
				for epoch in range(lowerCamelCase  ,     lowerCamelCase    ):
								with TorchTracemalloc() as tracemalloc:
												model.train()
												for step, batch in enumerate(lowerCamelCase    ):
																UpperCamelCase_:				Union[str, Any]						=    model(**lowerCamelCase    )
																UpperCamelCase_:				str						=    outputs.loss
																UpperCamelCase_:				List[str]						=    loss / gradient_accumulation_steps
																accelerator.backward(lowerCamelCase    )
																if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

																overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
								accelerator.print("""Memory before entering the train : {}""".format(bamb(tracemalloc.begin    )    )    )
								accelerator.print("""Memory consumed at the end of the train (end-begin): {}""".format(tracemalloc.used    )    )
								accelerator.print("""Peak Memory consumed during the train (max-begin): {}""".format(tracemalloc.peaked    )    )
								accelerator.print(
								    """Total Peak Memory consumed during the train (max): {}""".format(
								        tracemalloc.peaked + bamb(tracemalloc.begin    )    )    )
								UpperCamelCase_:				Optional[Any]						=    tracemalloc.peaked + bamb(tracemalloc.begin    )
								if args.peak_memory_upper_bound is not None:
												assert (
												    train_total_peak_memory[F'''epoch-{epoch}'''] <= args.peak_memory_upper_bound
												), "Peak memory usage exceeded the upper bound"

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
								with open(os.path.join(args.output_dir  ,     """peak_memory_utilization.json"""    )  ,     """w"""    ) as f:
												json.dump(lowerCamelCase  ,     lowerCamelCase    )







def 							A__       (    )	->						Optional[Any]:
				UpperCamelCase_:				Any						=    argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""    )
				parser.add_argument(
				    """--model_name_or_path"""  ,     type=lowerCamelCase  ,     default="""bert-base-cased"""  ,     help="""Path to pretrained model or model identifier from huggingface.co/models."""  ,     required=lowerCamelCase  ,     )
				parser.add_argument(
				    """--output_dir"""  ,     type=lowerCamelCase  ,     default="""."""  ,     help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""  ,     )
				parser.add_argument(
				    """--peak_memory_upper_bound"""  ,     type=lowerCamelCase  ,     default=lowerCamelCase  ,     help="""The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."""  ,     )
				parser.add_argument(
				    """--n_train"""  ,     type=lowerCamelCase  ,     default=3_20  ,     help="""Number of training examples to use."""  ,     )
				parser.add_argument(
				    """--n_val"""  ,     type=lowerCamelCase  ,     default=1_60  ,     help="""Number of validation examples to use."""  ,     )
				parser.add_argument(
				    """--num_epochs"""  ,     type=lowerCamelCase  ,     default=1  ,     help="""Number of train epochs."""  ,     )
				UpperCamelCase_:				Optional[int]						=    parser.parse_args()
				UpperCamelCase_:				Union[str, Any]						=    {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
				training_function(lowerCamelCase  ,     lowerCamelCase    )


if __name__ == "__main__":
							main()






